[下载]硕士学位论文 农村居民食物消费结构变动及其对粮食需求影响的实证分析

poor

I won't buy this folk's book anymore.

It is just my personal opinion. Of course, you can disagree with me.

Out of curiosity, I spent 100 bucks downloading the book "Basic Statistcis". I found that the uploaded file only contains the first six chapters of that book. There are 24 chapters in that book. Besides, that book is out of date. The current edition of the same book is the 4 ediction.

Can someone tell me? How dare does this guy charge $100 for an incomplete and out-of-date book? Think twice befopre you pay big money to download this guy's books.

[此贴子已经被作者于2006-5-2 10:07:32编辑过]

Applied Categorical & Nonnormal Data Analysis

Part 1 - Peliminary Topics

1. Introduction
   
2. Information in Contingency Tables
   
3. Review of OLS Regession
   
4. Collinearity Issues
   
5. Loglinear Regression Models
   

   Part 2 - Binary Response Models

6. Odds & Ends
   
7. Logistic Regression Models
   
8. More Logistic Regression
   
9. Model Fit
   
10. Logistic Diagnostics
    
11. Using Categorical Predictors (Courtesy of ATS)
    Note: Unit 11 is rough draft for an unfinished project.
    
12. Interactions in Logistic Regression
    
13. Perfect Prediction
    
14. Polynomial Logistic Regression
    
15. OLS versus Logistic
    
16. Probit Models
    
17. Interpreting Probit Coefficients
    
18. Complementary Log-Log Models
    
19. Conditional Logit Models
    
20. Bivariate Probit Models
    
21. Multivariate Probit Models
    
22. Binary Panel Data
    
23. Survey Logistic Regression
    

    Part 3 - Beyond Binary: Multinomial Response Models

24. Ordered Logit & Probit Models
    
25. Cut Points & Constants (Stata FAQ)
    
26. Multinomial Logit Models
    
27. Left/Right Equivalency
    
28. Ordinal Predictor Variables
    
29. Interpreting Logistic Regression in all its Forms(PDF) by William Gould
    
30. Discriminant Function Analysis
    

    Part 4 - Count Models

31. Poisson Models
    
32. Negative Binomial Models
    
33. Zero-inflated Count Models
    
34. Zero-truncated Count Models
    
35. Hurdle Models
    

    Part 5 - Survival Models

36. Introduction to Survival Analysis
    
37. Discrete-Time Survival Analysis
    
38. Proportional Hazards (Semiparametric) Model
    

    Part 6 - Other Topics

39. Generalized Linear Models
    
40. A Matter of Proportion
    
41. Relative Risk
    
42. Generalized Estimating Equations - Gausian
    
43. Generalized Estimating Equations - Binary & Count
    
44. Regression Models with Censored Data or Truncated Data
    
45. Selection Models
    
46. Quantile Regression
    
47. A Rasch Model Example
    
48. Latent Profile & Latent Class Models
    
49. Latent Class Analysis Stata Example
    
50. Instrumental Variables Regression
    
51. Regression with Measurement Error
    
52. Correspondence Analysis
    
53. The Process of Data Analysis

[此贴子已经被作者于2006-5-2 10:31:03编辑过]

Categorical Data Analysis with Graphics

Michael Friendly

This document provides several versions of my short course notes for Categorical Data Analysis with Graphics, offered through the Statistical Consulting Service at York University.

• Lecture slides (4up) --- PDF version
  
  • Part 1
    
  • Part 2
    
  • Part 3
• Lecture notes --- PDF version
  
• Lecture notes --- HTML version
  The HTML version contains an earlier version of the the text, tables, and character-based graphs of the printed version, and many, but not all, of the high-resolution graphs.

The main source for these materials is my book, Visualizing Categorical Data

If you want to learn more about categorical data analysis, there are several books and other resources I recommend:

• Agresti, A. (1990). Categorical Data Analysis. NY: Wiley.
  
• Christensen, R. (1990). :Log-Linear Models. Springer-Verlag.
  
• Everitt, B. S. (1992). The Analysis of Contingency Tables, London: Chapman & Hall (Monograph 45).
  
• Stokes, M.E., Davis, C.S., and Koch, G.S. (1995). Categorical Data Analysis Using the SAS(R) System. Cary, NC: SAS Institute.
  
• Robert Hanneman's Generalized Linear Models: An Introductioon

[此贴子已经被作者于2006-5-2 10:34:18编辑过]

Applied Categorical Data Analysis
EdPsych 590AT/Psych 593CA
C.J. Anderson
Spring 2006 http://www.ed.uiuc.edu/courses/EdPsy490AT/#lectures

[此贴子已经被作者于2006-5-2 10:38:38编辑过]

Longitudinal Data Analysis

Course Materials

• Course Syllabus for Autumn 2005

Lecture Notes

• Lecture 1 Notes
• Lecture 2 Notes
• Lecture 2 Supplement
• Comment on Stata commands tsset, iis and tis
• Lecture 3 Notes
• Handwritten Summary Slides on Exploratory Analysis for Longitudinal Data
• Lecture 4 Notes
• Lecture 5 Notes
• Introductory notes on using SAS
• Lecture 6 and Lecture 6a Notes
• Lecture 7 Notes
• Lecture 8 Notes
• Example of using Stata command xtmixed to fit random effects models (updated 10 Nov 05)
• Lecture 9 Notes (updated 07 Nov 05)
• Lecture 10 Notes
• Notes on Exploratory Data Analysis for Categorical Longitudinal Data
• Lecture 11 Notes
• Lecture 12 Notes
• Lecture 12a Notes
• Handwritten Summary Slides on Linear Models for Longitudinal Data
• Handwritten Summary Slides on Generalized Linear Models for Longitudinal Data

Multilevel Analysis/Hierarchical Linear Modeling
EdPsych 590CK/Psych 593CA
C.J. Anderson
Fall 2004

Last revised: 2/20/2006

General Information (12/6/04)

Announcements (11/16/04)

Lecture notes All are complete.

Computer Lab (11/18/04)

Homework (11/18/04)

Example analyses

Handy program and links

UCLA ATS Resources on Multilevel Modeling

Multilevel Modeling

• Guide to Web Resources for Multilevel Modeling thanks to by Kelvyn Jones, Myles Gould and SV Subramanian
• Course Notes on Multilevel Modeling from C.J. Anderson
• Hierarchical (multilevel) models for survey data
• Distinguishing Between Random and Fixed Effects
• Papers by William J. Browne
• Presentations on Multilevel Modeling by William J. Browne
• Software Reviews of Multilevel Analysis Packages
• Multilevel Modeling Discussion List
• Multilevel Modeling newsletters
• Longitudinal Data Analysis A course by Don Hedeker with Datasets and class notes including

HLM

• HLM Home Page (including)
  • Online Resources
  • FAQs

• HLM FAQs from UT Austin
• Multilevel Regression (featuring HLM) by Jason Newsom

MLwiN

• MLwiN Web Site (i.e. the Centre for Multilevel Modeling) including
  • MLwiN Manuals
  • Features of MLwiN
  • MLwiN Support (including FAQs)
  • Free Training Version of MLwiN
• An introduction to Multilevel Modelling using MLwiN by Leyland, A. H., and McLeod, A. . MRC Social and Public Health Sciences Unit, Glasgow: 2000; Occasional paper no. 1. (data is available with Free Training Version of MLwiN)
• Multilevel Analysis by Tom Snijders, including MLwiN Macros and Example MLwin Session

Mplus

• Mplus Home Page

Stata

• gllamm- Stata program to fit Generalised Linear Latent and Mixed Models

Power Analysis

• PINT is available from Tom Snijders' website.
• Optimal Design (power analysis for Multilevel Models) Via FTP site, see od.zip and od_manual.pdf

Hierarchical Modeling for the Social Sciences

Marijtje van Duijn

Course description

In many cases data collected by social scientists exhibit some form of clustering or hierarchical structure, due to sampling (students within classes) or other longitudinal or nested designs (measurements of subjects over time). The resulting complex variance structure requires (regression) models that take into account multiple sources of variability. Hierarchical linear modeling, also known as multilevel analysis provides the appropriate methodology, and is applied extensively in the social sciences.

The goal of the course is not only to achieve a good understanding of the hierarchical models and their statistical intricacies, but also to have practical experience in applying multilevel modeling. Several commercial packages are available for this latter purpose, but in the course the free software R will be used.

Prerequisites

It is assumed that students have completed a statistical sequence (such as SOC 424-426), and a regression or an applied regression course (such as CS&SS 504). It is also recommended that students have some familiarity with basic calculus (differentiation and integration), matrix algebra (matrix addition, multiplications, and inversion), and probability theory. Some familiarity with computing packages R or S-Plus would be helpful as well.

Structure of the Course

There will be a two lectures per week. The lecture on Thursday will sometimes be a laboratory session.

Textbooks

Tom Snijders and Roel Bosker (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London: Sage Required.

José C. Pinheiro and Douglas M. Bates (2000). Mixed-Effects Models in S and S-PLUS. New York, NY: Springer. Required.

Optional texts

Agresti, A. (2002). Categorical Data Analysis. New York, NY: John Wiley & Sons.

Faraway, J.J. (2005). Linear Models with R. Boca Raton, FL: Chapman & Hall/CRC. An older version available online at http://cran.us.r-project.org/doc/contrib/Faraway-PRA.pdf

Hox, J. (2002). Multilevel models. Mahwah, NJ: Lawrence Erlbaum Associates.

Raudenbush, S.W., & Bryk, A.S. Hierarchical Linear Models: Applications and Data Analysis Methods. 2^nd Edition. Thousand Oaks, CA: Sage.

Singer, J.D., & Willett, J.B. (2003). Applied Longitudinal Data Analysis. Modeling change and Event Occurrence. New York, NY: Oxford University Press.

Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, FL: Chapman & Hall/CRC.

Venables, W.N., Smith, D.M., & the R Development Core Team (2004). An Introduction to R. Revised and updated. Bristol: Network Theory Ltd. Also available online at http://www.r-project.org/

Verbeke, G. & Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York, NY: Springer.

Computer Usage and Software

The R/S-plus software will be used for data exploration and analysis. Other available software for hierarchical linear modeling or multilevel analysis may be used as well. See links for further information.

Course Requirements and Grades

There will be a number of homework assignments and exercises both the theory and real data analysis. Students will be graded on a scale of 1 to 10 for each assignment. This will be 50% of the grade. Homework that is not handed in on time will receive no points.

Although it is allowed to discuss homework problems with others, each student is required to prepare and submit solutions (including computer work) to the assignments and project on their own; solutions prepared “in committee” are not acceptable. Duplication of homework solutions and computer output prepared in whole or in part by someone else is not acceptable and is considered plagiarism.

There will be a final take home exam worth 50% of the grade. This can be exchanged for a project entailing a complete analysis of a data set that you may have access to. If you prefer to do such a project, get in touch with me at an early stage so that we can work out the details.

Please use a text editor to type up your homework assignments. Unless specifically requested, never submit raw computer output pages, but cut out the appropriate parts and neatly tape it onto your homework paper, or cut-and-paste it in your write-up. Be sure to use correct labels or titles for all tables, plots, etc.

[此贴子已经被作者于2006-5-2 11:33:05编辑过]